My doubt is whether it is correct for me to consider the two factors of my experiment to remove the autiliers or do I just consider the main factor, follow the model I thought of using:
class BLOC factor1 factor2;
model GMDadap = BLOC factor1 factor1 * factor2;
output out = dois residual = x_res student = x_stu ;
proc means n mean;
proc univariate normal plot data=dois;
var x_stu ;
This is a reasonable way to approach outlier detection. Of course, there are plenty of other methods, including methods if the data is not normally distributed (such as box plot outliers), and even multivariate outlier detection. And possibly dozens of other methods.
There's no universally agreed upon method of detecting outliers. I think if you are going to fit a model with two factors, the outliers in Y ought to be detected via residuals, which means to me that all terms in the model should be used.
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